Blind and Compact Denoising Network Based on Noise Order Learning

Keunsoo Ko, Yeong Jun Koh, Chang Su Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

A lightweight blind image denoiser, called blind compact denoising network (BCDNet), is proposed in this paper to achieve excellent trade-offs between performance and network complexity. With only 330K parameters, the proposed BCDNet is composed of the compact denoising network (CDNet) and the guidance network (GNet). From a noisy image, GNet extracts a guidance feature, which encodes the severity of the noise. Then, using the guidance feature, CDNet filters the image adaptively according to the severity to remove the noise effectively. Moreover, by reducing the number of parameters without compromising the performance, CDNet achieves denoising not only effectively but also efficiently. Experimental results show that the proposed BCDNet yields state-of-the-art or competitive denoising performances on various datasets while requiring significantly fewer parameters.

Original languageEnglish
Pages (from-to)1657-1670
Number of pages14
JournalIEEE Transactions on Image Processing
Volume31
DOIs
Publication statusPublished - 2022

Keywords

  • Image denoising
  • convolutional neural network
  • lightweight design
  • order learning

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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